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Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles

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URI: https://hdl.handle.net/10016/38469
ISSN: 0031-3203
DOI: https://doi.org/10.1016/j.patcog.2022.109158
UXXI: AR/0000031345
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Optimum_PR_2023.pdf (805.04 KB)
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2023-03
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Elsevier
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Abstract
Asymmetric label switching is an effective and principled method for creating a diverse ensemble of learners for imbalanced classification problems. This technique can be combined with other rebalancing mechanisms, such as those based on cost policies or class proportion modifications. In this study, and under the Bayesian theory framework, we specify the optimal decision thresholds for the combination of these mechanisms. In addition, we propose using a gating network to aggregate the learners contributions as an additional mechanism to improve the overall performance of the system.
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Bayesian framework, Ensembles, Rebalancing techniques, Imbalanced classification, Label switching
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Gutiérrez-López, A., Gonzalez-Serrano, F., & Figueiras-Vidal, A. R. (2023). Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles. Pattern Recognition, 135, 109158.
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